Imaging system and method for imaging objects with reduced image blur

ABSTRACT

An imaging device is presented for use in an imaging system capable of improving the image quality. The imaging device has one or more optical systems defining an effective aperture of the imaging device. The imaging device comprises a lens system having an algebraic representation matrix of a diagonalized form defining a first Condition Number, and a phase encoder utility adapted to effect a second Condition Number of an algebraic representation matrix of the imaging device, smaller than said first Condition Number of the lens system.

This is a Continuation of application Ser. No. 15/184,592, filed Jun.16, 2016, which in turn is a Continuation of application Ser. No.13/231,412, filed Sep. 13, 2011, which in turn is a continuation in partof PCT/IL2010/000214, filed Mar. 13, 2014, which claims the benefit ofU.S. Provisional Applications Nos. 61/293,782, filed Jan. 11, 2010;61/167,194, filed Apr. 7, 2009; and 61/159,841, filed Mar. 13, 2009. Thedisclosure of the prior applications is hereby incorporated by referenceherein in its entirety.

FIELD OF THE INVENTION

This invention is generally in the field of imaging techniques andrelates to an imaging device and system utilizing the same aimed toimprove image quality by phase coding of light.

REFERENCES

The following references are used for facilitating understanding of thebackground of the present invention:

-   1. T. P. Costello and W. B. Mikhael, “Efficient restoration of space    variant blurs from physical optics by sectioning with modified    wiener filtering”, Digital and image processing, 13, 1-22, (2003).-   2. H. C. Andrews and C. L. Paterson, “Singular value Decomposition    and digital image processing”, IEEE. Trans. on ASSP. 24, 1, 26-53,    (1976).-   3. J. W. Goodman, Introduction to Fourier Optics (Mcgraw-Hill 1968).-   4. M. Bertero and P. Boccacci, “Introduction to inverse problems in    imaging”, No. 86, 252, IOP, (1998).-   5. R. W. Gerschberg and W. O. Saxton, A practical algorithm for the    determination of phase from image diffraction plane picture, optic,    237-246, V35, N2, (1972).-   6. R. Gonsalves, Phase retrieval and diversity in adaptive optics,    Opt Eng, V21 829-832 (1982).-   7. R. A. Gonsalves, et-al, Joint estimation of object and    aberrations by using phase diversity J. Opt. Soc. Am. A, 1072-1085,    V9 (1992).-   8. R. A. Gonsalves, Nonisoplanatic imaging by phase diversity, Opt    Lett. V19, 493-495, (1994).-   9. M. G. Lofdahl, Multiframe deconvolution with space variant point    spread function by use of inverse filtering and fast Fourier    transform, V26 4686-4693, App Opt, (2007).-   10. R. G. Paxman, J. H. Seldin, ‘Phase-Diversity Data set and    Processing Strategies, ASP Conf, V183, (1999).

BACKGROUND OF THE INVENTION

Image enhancement, expressed in improved image quality, is anever-lasting quest in the construction of optical systems. Image qualitymay be represented by one or more characterizing parameters such asdepth-of-focus, distortion, aberrations-related-blur, SNR or resolution,and the image improvement may be the result of an improvement in one ormore of such parameters. Thus, for example, various optical componentsmay be incorporated in an optical system with the aim of reducingaberrations. Various types of lens coating or other optical correctors(e.g. lenses) may be used with an imaging lens unit to reduce chromaticand/or spherical aberrations (coma or other blur sources) caused by theimaging lens unit.

In recent years, image processing techniques are also becoming a commontool used to enhance images, and significant improvement of imagesobtained from a given optical system may indeed be achieved byperforming appropriate image post-processing on such images. Theminiaturization of computerized processors and the subsequentproliferation of such processors in commercial commodities such asdigital and video cameras, cellular phone cameras etc., have expeditedthe incorporation of computerized image enhancement utilities in suchdevices.

It is known to use an aperture coding (e.g. phase-coding) within theoptical system and/or appropriate image processing, to improve the imagequality. For example, U.S. Pat. No. 6,842,297 discloses Wavefront CodingOptics, which applies a phase profile to the wavefront of light from anobject to be imaged, retain their insensitivity to focus relatedaberrations, while increasing the height of the resulting ModulationTransfer Functions (MTFs) and reducing the noise in the final image. US2008/0131018 describes an image reconstruction technique utilizingalgorithm applied on phase encoded image data to obtain an image inwhich the effects of phase perturbation are removed. This techniqueincludes estimation of a degree of defocus in the obtained image,adjustment of the reconstruction algorithm to take into account theestimated degree of defocus, and application of the adjustedreconstruction algorithm to obtain a restored image.

GENERAL DESCRIPTION

There is a need in the art to facilitate imaging of an object to obtainimproved image quality, e.g. signal-to-noise (SNR), resolution e.g.being free of or at least with significantly reduced blur and/or defocuseffects. The present invention provides a novel imaging arrangement andan imaging system capable of improving the image quality.

The invention is associated with the inventors' understanding of thefollowing: Defocus related blur is generally space invariant, namely theblur associated with the effect of defocus is substantially similaracross the object plane. This means that the smear of an imaged pointwill be similar for different points in the image plane. However, asignificant number of important blur sources are space variant. In otherwords, the blur associated with the image of different field pointsdiffers according to the location of the point with respect to theoptical axis of the optical system. Such space variant blur sources maybe for example coma, astigmatism, field curvature, distortion etc. Dueto the different nature of the blur sources, methods of imageenhancement that may be applied in cases of space invariant blur mightnot be suitable for cases of space variant blur. For example, in case ofspace variant blur, the well established methods of Fourierde-convolution cannot be used, and other image processing methods areneeded. For cases where optical system aberrations are coma-like, aninversion method based on Mellin transform and one dimensional Fouriertransform can generally be used. In the general case of space variantblur, under the limitation of boundary condition continuance (to preventedge effects), an image can be divided into a mosaic of pseudo-spaceinvariant zones [1]. If the mosaic method is not suitable, other methodsshould be considered.

The invention utilizes algebraic representation of an imaging system,suitable for space variant image restoration. An imaging optics havingan algebraic representation can be described by a Point Spread Function(PSF) matrix. Hence, an object in the object plane and its image in theimage plane can be represented by vector columns o and i, respectively,and the optics itself can be represented by a matrix H, the columns ofwhich are the PSF for each field point. In this representation, thediscrete image i may be obtained by the multiplication of the vector oby matrix H:i=H·o  (1)

Thus, a blur-free reconstruction of the object might theoretically beobtained by multiplying the image vector i by the inverse of the opticsmatrix, H⁻¹:i ^(restored) =H ⁻¹ ·i  (2)

However, image enhancement through the above image post-processingtechnique faces limitations when applied to conventional opticalsystems. For example, noise generated or collected by the optical systemand added to the image may hinder the matrix inversion process.Additionally, the numerical processes involved in the image enhancement,for example, when matrix inversion is employed, might often increase thenoise in the final image due to ill matrix condition.

Alternatively or additionally, such mathematical tool as Singular ValueDecomposition (SVD) can be applied for image enhancement [2]. Morespecifically, when a representing matrix H of the optical system is notinvertible or is ill-conditioned, SVD allows providing an approximateinverse to the matrix by truncating those matrix parts which areassociated with the matrix low singular values. Inversion can also beexecuted by regularization or by least square method. However, it shouldbe emphasized that all the above methods are sensitive to the matrixcondition, as is explained in detail further below.

The invention utilizes affecting the algebraic representation matrix ofan optical system (its imaging arrangement including one or more lensesor lens regions) to improve the matrix condition, and thereby to allow amore accurate image restoration by post-processing. The effect on thealgebraic representation matrix to be achieved within the optical systemis aimed at rendering the algebraic representation matrix rank-full (andimproving the condition) and hence invertible, and additionally oralternatively, less susceptive to errors under matrix inversion in thepost-processing procedure.

It should be understood that generally when an algebraic representationmatrix of an optical system (as well as any other linear system) isrank-full, the matrix usually has a diagonal form in which all numberson the main diagonal are different than zero, and the matrix isinvertible. When an algebraic representation matrix of an optical systemis rank-deficient, a diagonal form of the matrix may generally have atleast one or a few zeros on the main diagonal, and the matrix istherefore not invertible. In either case, i.e. operating with an opticalsystem having rank-full or rank-deficient algebraic representationmatrix, the present invention affects algebraic representation matrix tofacilitate its inversion by improving its condition.

Thus, in its general aspect, the present invention provides a monitoringsystem for monitoring operation of a linear system, being a mechanicaland/or optical. The monitoring system comprises a main measurementsystem characterize by an algebraic representation matrix H, anauxiliary measurement system characterized by an auxiliary system matrixO, and a control unit. The main and auxiliary measurement systems areconfigured and operable for performing a finite series of n measurementsfrom n locations of said linear mechanical system. The control unit isconfigured and operable to process data indicative of said measurementsperformed by the auxiliary system according to predetermineddecomposition transformation matrixes and to sum main system measureddata with the processed auxiliary data, thereby obtaining measured datacorresponding to an improved conditioned parallel measurement system

More specifically, considering for example an imaging arrangement,according to some embodiments of the invention, it provides for usingphase encoder applied to a light field passing through an optical system(lens system). The lens system has a certain algebraic representationmatrix of a diagonalized form defining a first Condition Number. Thephase encoder is configured to effect a second Condition Number of analgebraic representation matrix of the imaging arrangement (lens systemand phase encoder), where the second Condition Number is smaller thanthe first Condition Number.

In some embodiments, the phase encoder comprises a first pattern whichis located in a first region, aligned, along an optical axis of the lenssystem, with a part of the effective aperture of the imagingarrangement, leaving a remaining part of the effective aperture free ofthe first pattern. The geometry of the first region and configuration ofthe first pattern are selected to define a predetermined first phasecorrection function induced by the first pattern onto the light fieldpassing therethrough to achieve said effect of Condition Number. Thephase encoder may be configured for additionally encoding the amplitudeof light passing therethrough (e.g. the encoder has an additionalamplitude pattern, i.e. a transmission profile).

The geometry of the first region and configuration of the first patternare preferably selected such that the first phase correction functioncorresponding to the phase encoder satisfies a predetermined conditionwith respect to a phase correction function induced by the remainingpart of the effective aperture (e.g. by the lens). Preferably, thepredetermined condition is such that the first phase correction functionvalue, along the first region, is not smaller than or does not exceedthe phase correction function induced by the remaining part of theeffective aperture.

It should be understood that the phase correction function P(x,y) whichis induced by the phase encoder for improving the matrix conditionaccording to this invention, can have various shapes and symmetries. Itshould further be understood that a region where the phase correctionfunction P(x,y) varies (continuously or step-wise), corresponds to thepatterned region of the phase encoder.

Referring to the patterned region of the phase encoder, the followingshould be noted: In some embodiments, the patterned region can berestricted to a rim (periphery) of the effective aperture of the opticalsystem. In some other embodiments it could be located away from the rimof the effective aperture or in its center. In some further embodiments,it could have full rotational symmetry surrounding or being enclosed bya lensing region, or can have only discrete rotational symmetry. In yetfurther embodiments, the patterned region and the corresponding phasecorrection function P(x,y) can have reflection symmetry. Likewise,according to yet further embodiments of this invention, phase correctionfunction P(x,y) can have yet further shapes and symmetries, foraccomplishing the above mentioned improvement of the matrix condition.

According to some embodiments of the invention, it provides for using“regular” optical systems (i.e. systems with no phase coding), beingintegral within the same optical unit or not, for imaging the sameobject (concurrently or not) with different numerical apertures of lightcollection. Then, image processing is applied to the so-captured images.The image processing utilizes duplicating and shifting of the image,corresponding to the first numerical aperture of light collection, withrespect to the other image, corresponding to the other (second)numerical aperture of light collection, and then further processing theso-obtained multiple image data. The images are combined into one imagecorresponding to that obtainable with an optical system with theimproved condition matrix representation.

It should be understood, that the invention may be associated with asingle optical system, successively functioning as a main system and anauxiliary system. This is the so-called “multiple-exposure” (e.g.“double-exposure”) technique.

The invention, in its either aspect, allows using in optical system animaging lens having a large Numerical Aperture (NA). Such an opticalsystem will work in weak luminance but would suffer from space variantblur due to large aberrations. For example, by employing the invention,operation of the optical system having an imaging lens with high NA of0.35 can be improved to have the image quality of a system using animaging lens with NA of 0.29.

Thus, according to one broad aspect of the invention, there is providedan imaging arrangement (device) having an effective aperture andcomprising: a lens system having an algebraic representation matrix of adiagonalized form defining a first Condition Number, and a phase encoderutility adapted to effect a second Condition Number of an algebraicrepresentation matrix of the imaging device, smaller than said firstCondition Number of the lens system.

In some embodiments, the phase encoder comprises a first pattern whichis located in a first region aligned, along an optical axis of the lenssystem, with a part of said effective aperture, leaving a remaining partof the effective aperture free of said first pattern. The geometry ofsaid first region and configuration of said first pattern therein areselected to define a predetermined first phase correction functioninduced by said first pattern into light passing therethrough,

Preferably, the geometry of the first region and configuration of thefirst pattern are selected such that the first phase correction functionsatisfies a predetermined condition with respect to a phase correctionfunction induced by the remaining part of the effective aperture. Thepredetermined condition may be such that the first phase correctionfunction value, along the first region, is not smaller than or does notexceed the phase correction function induced by said remaining part ofthe effective aperture.

In some embodiments, the algebraic representation matrix is a PointSpread Function (PSF) matrix. The diagonalized form of the algebraicrepresentation matrix is a Singular Value Decomposition (SVD), or may bea Fourier Decomposition or other diagonalized form.

The first region having said first pattern may be arranged around saidoptical axis, e.g. having a ring-like geometry.

The first pattern may be a continuous pattern along the first region, ormay be in the form of a plurality of patterned segments arranged in aspaced-apart relationship along said first region. The patternedsegments of the first pattern may comprise at least one lens segment, oran array of lens segments.

The first phase correction function may be rotationally symmetric aroundthe optical axis; or may vary along the first region being kept eitherlarger or smaller than that of the phase correction function within saidremaining part of the effective aperture.

The first region may be located within a periphery of the effectiveaperture, while the remaining part is located within a central region ofthe effective aperture. Alternatively, the first region may be locatedwithin a central region of the effective aperture surrounded by saidremaining part.

The first region may be located substantially at an exit pupil of thelens system. The first region may be located upstream or downstream ofthe lens system along said optical axis; or within the lens system beingupstream or downstream of the main plane thereof.

The phase encoder may be integral with a lens of the lens system. Thefirst pattern may be in the form of an array of spaced-apart regions ofa material of a refractive index different from that of the lens. Thefirst pattern may be in the form of a profile of a varying surface of alens of the lens system.

According to another broad aspect of the invention, there is provided animaging system comprising the above-described imaging arrangement, adetection unit, and a control system. The latter is configured andoperable to process and analyze data indicative of images detected bythe detection unit by applying to the data inversion of a predeterminedPSF matrix corresponding to the algebraic representation matrix of anoptical system having space-variant aberrations.

According to yet further aspect of the invention, there is provided animaging system comprising:

-   -   an imaging device comprising a basic lens system and a phase        encoder and defining an effective aperture, the basic lens        system having an algebraic representation matrix of a        diagonalized form defining a first Condition Number, the phase        encoder being configured to effect a second Condition Number of        an algebraic representation matrix of the imaging device, the        phase encoder comprising a first pattern which is located in a        first region aligned, along an optical axis of the lens system,        with a part of said effective aperture, leaving a remaining part        of the effective aperture free of said first pattern, geometry        of said first region and configuration of said first pattern        therein being selected to define a predetermined first phase        correction function induced by said first pattern onto light        passing therethrough, the second Condition Number of the imaging        device being thereby smaller than said first Condition Number of        the lens system;    -   an image detection unit, and    -   a control system configured and operable to process and analyze        data indicative of images detected by said detection unit to        restore an image of an object with significantly reduced image        blur.

The invention also provides an imaging system comprising:

-   -   an optical system comprising first and second lens systems with        the common field of view, wherein a first lens system has a        first optical axis and a first Numerical Aperture; and a second        lens system has a second optical axis parallel to and        spaced-apart from said first optical axis and a second Numerical        Aperture smaller than said first Numerical Aperture,    -   an imaging detection unit for detecting light collected by the        first and second lens systems and generating image data        indicative thereof; and    -   a control system for receiving and processing said image data,        the processing comprising duplicating and shifting a second        image, corresponding to the light collected by the second lens        system, with respect to a first image, corresponding to light        collected by the first lens system, and producing a        reconstructed image data.

The invention also provides an image processing method comprising:receiving image data, said image data comprising a first data portioncorresponding to a first image of a region of interest with a certainfield of view and a Numerical Aperture of light collection and a seconddata portion corresponding to a second image of said region of interestwith said certain field of view and a Numerical Aperture of lightcollection; and processing the image data by duplicating and shiftingthe second image with respect to the first image and producing areconstructed image data. The Numerical Apertures corresponding to thefirst and second images may or may not be the same.

In its further aspect, the invention provides a method for reducingimage blur in an optical system, the method comprising:

-   -   obtaining an algebraic representation matrix of an optical        system;    -   diagonalizing said algebraic representation matrix, thus        obtaining a first diagonal matrix S;    -   selecting n eigenvalues from a total of N eigenvalues of said        diagonal matrix S, which are located in n positions,        respectively, along a diagonal of said diagonal matrix S.    -   selecting a second diagonal matrix ΔS having substantially the        same size as said first diagonal matrix S, said second diagonal        matrix ΔS having n numbers on its diagonal in positions        corresponding to said n respective positions in the first        diagonal matrix S, and having all other numbers equal zero.    -   applying an inverse operation of said diagonalization on the        matrix sum of first diagonal matrix S and second diagonal matrix        ΔS, thus obtaining a third matrix O.    -   applying a correction function on light passing through the        optical system, such that an algebraic representation matrix of        the optical system affected by said correction function,        approximates matrix O.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to understand the invention and to see how it may be carriedout in practice, embodiments will now be described, by way ofnon-limiting example only, with reference to the accompanying drawings,in which:

FIG. 1 is a schematic illustration of an imaging system suitable forimplementing the present invention;

FIGS. 2A and 2B show two examples respectively of an imaging device ofthe invention including an imaging lens and a phase encoder.

FIG. 3A shows more specifically an example of the imaging device wherethe phase encoder is located on the rim ring of the device surroundingthe lens.

FIG. 3B is a graph showing the normalized singular values of a SingularValue Decomposition of an imaging lens' PSF matrix, arrange according totheir values in a decreasing order;

FIG. 3C shows a visual representation of Point Spread Function of aBMSD, arranged according to the corresponding field points in 25representative field points;

FIGS. 4A to 4D compare Mean Square Error Improvement Factor (FIGS. 4Aand 4C) and performance (FIGS. 4B and 4D) in the imaging system of thepresent invention with the conventional one (with no phase encoder),FIGS. 4A and 4C show graphs comparing the Mean Square Error ImprovementFactor (MSEIF) as a function of Signal to Noise Ratio (SNR) in twooptical systems, the imaging system of the present invention with theconventional one, and FIGS. 4B and 4D show examples comparing theperformance of the imaging system of the present invention with theconventional one;

FIGS. 5A to 5D show other examples of the comparison between theperformance of the invented and conventional imaging systems, FIGS. 5Aand 5C show imaging performance of the invented and conventional imagingsystems, and FIGS. 5B and 5D show graphs corresponding to examplescomparing the Mean Square Error Improvement Factor (MSEIF) as a functionof Signal to Noise Ratio (SNR) in the imaging system of the presentinvention with that of the conventional system (with no phase encoder);

FIGS. 6A to 6D show exemplary images obtained with the invented andconventional imaging techniques (FIGS. 6A and 6C) and theircorresponding MSEIF graphs (FIGS. 6b and 6D) FIGS. 6B and 6D show thecorresponding graphs comparing the MSEIF as a function of SNR in theimaging system of the present invention with that of the conventionalsystem (with no phase encoder);

FIG. 7A is graph showing an example comparing the normalized singularvalues of Singular Value Decompositions of the PSF matrices of threeoptical systems: conventional imaging system with no encoder but withthe in-focus configuration, the same system with its out-of-focusconfiguration, and the system of the invention for correcting thefocusing of the conventional system;

FIG. 7B is a graph comparing the MSEIF as a function of SNR in the twooptical systems—the conventional out-of-focus and the same in itsimproved version implementing the invention;

FIG. 7C shows the corresponding imaging performance of said two systems;

FIG. 8 is an example of a flow diagram of a method of the presentinvention for phase-encoding the light involved in the imaging byinducing a phase correction;

FIGS. 9A to 9F illustrate progress of the system condition number andcorresponding phase correction functions, FIGS. 9A and 9D show graphsexemplifying the progressing of the system condition number as afunction of calculation iteration of phase encoding function, and FIGS.9B-9C and 9E-9F exemplify phase correction functions induced by phaseencoders corresponding to the examples of FIGS. 9A and 9D;

FIGS. 10A to 10D show imaging results and MSEIF graphs for conventionalimaging system and the system of the invention, each of FIGS. 10A and10C show imaging results of two systems, one being the conventionalsystem and the other being the invented system implemented using theprinciples illustrated in FIGS. 8-9C, and FIGS. 10B and 10D show graphscomparing the MSEIF as a function of SNR in the two optical systemscorresponding to FIGS. 10A and 10C;

FIGS. 11A and 11B shows yet further example of an imaging deviceincluding a lens and a phase encoder, where the phase encoding patternhas an array of spaced-apart lenses;

FIG. 12 is a schematic illustration of an example of an imagingtechnique according to another aspect of the invention;

FIG. 13A compares images obtainable with the conventional technique andthat of FIG. 12;

FIG. 13B is a graph comparing pixels grey level of three images: idealpicture of the object (object itself) and images of FIG. 13A;

FIG. 13C exemplifies an object ensemble used in the simulations;

FIG. 13D compares the restoration average MSEIF ensemble results as afunction of the main system SNR for the “study case” restoration “as is”(i.e. imaging device having the main lens system only, with no auxiliarylens) and the “trajectories” or “shifted lenses” technique utilizing asimple auxiliary lens;

FIG. 13E exemplifies how the principles of the present invention cangenerally be used in any linear system, optical or not;

FIG. 14 shows a relation between the image shift and the trajectoriesmatrix in a 6×6 matrix;

FIGS. 15A and 15B show the “transformation” based approach for the 6×6PSF matrix for 3×2 FOV, where FIG. 15A presents the pixel confined case(“perfect” auxiliary lens), and FIG. 15B presents the “blurredtransformation” with a blurred auxiliary lens assuming a 2×2 kernel(presented in the bottom right);

FIGS. 16A and 16B show restoration average MSEIF ensemble results,presenting the average MSEIF in the following systems: the system havingthe “main lens” without additional optics; the system with the“Rim-ring” parallel optics design, the system with the parallel opticsusing the “Blurred Trajectories”, all being restored by theregularization method;

FIGS. 17A and 17B exemplify typical images and restorations byregularization, all in main SNR=45 [db], for the following: the studycase image (SC-Img.) without parallel optics; auxiliary lens image(Aux-Img), the object (Obj.), the study case restoration byregularization (SC-Res.), the Rim-ring restoration (RR-Res.), and theBlur Trajectories restoration (Traj-Res.);

FIG. 18 illustrates the main lens design used in simulations, where themain lens is subject to nominal Seidel aberrations and to a 176 μmdefocus;

FIG. 19 shows the “main” system singular values graph and the chosen(target) BMSD matrix used in the simulations;

FIG. 20 presents a comparison between the mean MSEIF results ofregularization method for three systems: the “Blurred Trajectories”(Traj), the “Rim-ring” with quadratic phase coefficient, and the “studycase” (SC) without additional optical design;

FIG. 21 exemplifies typical “object”, “main” system images, “auxiliary”system image, “Blurred Trajectories” restoration, “study case”restoration without additional optical design (as is), and “Rim-ring”restoration, all in SNR=45 [db];

FIG. 22 shows the system target BMSD composed from the last 50 eigenmatrixes;

FIG. 23 illustrates the calculation of the average filter, In figureevery forth PSF(x,y,i) is presented after appropriately shifted.

FIG. 24 shows the application of the average value (target PSF) of theensemble presented in FIG. 23;

FIG. 25 shows the phase profile for the rim ring implementation forimproving condition number in the example of FIGS. 22-24;

FIGS. 26A to 26D show MSEIF profiles and corresponding image restorationresults, FIGS. 26a and 26C show MSEIF profiles for the imaging devicewhere the main lens system is and is not equipped with an Average PPRim-ring filter, and FIGS. 26B and 26D show image restoration by matrixinversion for lens with and without Average PP Rim-ring filter in SNR=60[db].

DETAILED DESCRIPTION OF EMBODIMENTS

Referring to FIG. 1, there is schematically illustrated an imagingsystem, generally designated 1, suitable to be used for carrying out theinvention. Imaging system 1 includes an imaging arrangement (device) 2for collecting light from an object plane 3 and producing acorresponding image in an image plane; a detection unit 4 located in theimage plane for collecting light from imaging device 2 and generatingimage data indicative thereof; and a control unit 6 for processing theimage data.

Imaging device 2 includes a lens system 10 (one or more lenses) definingan optical axis OA of light propagation; and a corrector utility 20applying a correction function on light passing through the imagingdevice, which in the present example is constituted by phase encoder 20for applying a phase correction function, the configuration of whichwill be described further below. Imaging device 2 is configured andoperable according to the invention for reducing image blur created bythe lens system. It should be understood that a lens may be any opticalelement having a lensing effect suitable for use in imaging, e.g. alensing mirror.

The lens system and phase encoder are arranged together to define aneffective aperture EA of the imaging device 2. The lens system 10 is attime referred to herein below as a main system or main lens or mainoptics or first system. The main system has an algebraic representationmatrix of a diagonalized form defining a first Condition Number. Thephase encoder 20 is at time referred to herein below as an auxiliarysystem or second system or auxiliary lens or auxiliary consideredtogether with a respective portion of the main lens). The phase encoder20 is configured to produce a second Condition Number of an algebraicrepresentation matrix of the imaging device 2, smaller than the firstCondition Number of the main lens matrix.

As will be exemplified further below, the phase encoder 20 may beconfigured as a mask defining a first pattern located in a first region,which is aligned (along the optical axis of the lens system) with a partof the effective aperture, leaving a remaining part of the effectiveaperture free of the first pattern. The geometry of the first region andconfiguration of the first pattern therein are such as to define apredetermined first phase correction function induced by the firstpattern onto light passing therethrough.

Control unit 6 typically includes a computer system including inter aliaa processor utility, data input/output utilities, data presentationutility (display), etc. The phase encoder 20 and control unit 6 operatetogether to produce a high-quality image. The imaging device 2 equippedwith the phase encoder 20 appropriately distorts (codes) the wavefrontof light collected from the object, and data corresponding to suchdistorted image is then digitally processed by the control unit torestore a distortion-free image, with improved quality.

An algebraic representation of an imaging device with no phase encoder(i.e. an imaging lens system) can be described by a matrix H, thecolumns of which are the PSF for each field point in vectorrepresentation. Accordingly, said imaging device may be represented bymatrix H consisting of L×L elements where L=m×n, for the n×m pixelrepresentation of the image.

One way to diagonalize the matrix H is to apply to it a Singular ValueDecomposition (SVD). According to this method, the singular values of Hare square-roots of the eigenvalues of the matrix H·H^(t), where H^(t)is the so-called transposed matrix of H. According to this method, H maybe represented as [2]:H=U·S·V ^(t)  (3)where matrices U and V are found by solving the following eigenvalueproblems:H·H ^(t) =U·Δ·U ^(t)  (4)H ^(t) ·H=V·Δ·V ^(t)  (5)U _(L×L)=[u ₁ ,u ₂ , . . . ,u _(L)]  (6)V _(L×L)=[v ₁ ,v ₂ , . . . ,V _(L)]  (7)(u_(i) and v_(i) are the vector columns of U and V, respectively). Asingular value matrix S can then be obtained from matrix Δ, S=Δ^(1/2).

It should be noted that there are usually many ways, in addition to SVD,to diagonalize matrix H. The present invention may be employed inconjunction with any diagonalization procedure of the representingmatrix H which results in a diagonal matrix S, and the principles of theinvention are not limited to the example described herein.

Returning to SVD-based approach, the positive square roots of theeigenvalues of Δ are the eigenvalues σ_(i) in S and are termed also thesingular values in H. The order of the rows of S is such that σ₁ areordered from high to low:

$\begin{matrix}{{S_{L \times L} = \begin{bmatrix}\sigma_{1} & 0 & 0 & 0 \\0 & \sigma_{2} & 0 & 0 \\0 & 0 & \ddots & \vdots \\0 & 0 & \ldots & \sigma_{L}\end{bmatrix}},\left| {\sigma_{L} \leq \ldots \leq \sigma_{2} \leq \sigma_{1}} \right.} & (8)\end{matrix}$One of the figures of merit for the matrix H is a Condition Numberk_((H)), defined by a ratio as in (9) and (10) below:

$\begin{matrix}{{k_{(H)} = \left. \frac{\left\langle \sigma_{m} \right\rangle}{\left\langle \sigma_{n} \right\rangle} \middle| {\sigma_{n} \leq \sigma_{m}} \right.},{\forall m},{n.}} & (9)\end{matrix}$Here

denotes a moment of any order of weighted averaging,

σ_(m)

is such an averaging on a group of the selected high singular values and

σ_(n)

is such an averaging

on a group of selected low singular values. One example of a conditionnumber is thus the ratio of the highest to lowest singular values of H:k _((H))=σ₁/σ_(L)  (10)It should be understood that when the matrix S is a result of a generaldiagonalization procedure of the matrix H (rather than SVD), then σ_(m)and σ_(n) denote the absolute values of the eigenvalues of S.

Considering the SVD-based approach, all the eigenvalues of S are real,and are smaller than or equal to one and non-negative, hence k_((S)) isalways greater than or equal to one. Generally, the higher the ConditionNumber the lower (or worse) the condition of the matrix, and the worsethe system immunity to additive noise with respect to the matrixinversion. And if, for example, H is not invertible (namely at least oneof its singular values is zero), then k is infinite. In an absolutelynoise-free system, the image inversion is always possible even in veryill conditioned (high condition number) system; however introduction ofnoise to ill condition system will corrupt the image inversionrestoration results. Thus, improving the matrix condition improves thesystem immunity to noise and allows performing image restoration in thepresence of noise. Performing image inversion in matrix notationprovides blur reduction in the image. Hence, improving the matrixcondition can be an effective indication of improving image quality byimage reconstruction in actual optical systems.

Turning back to FIG. 1, the imaging device 2 of the present inventionpresents an “improved” device comprising a lens system and a phaseencoder which improves the condition of the entire imaging device (ascompared to that of the lens system only, and no phase encoder) bymaking a Condition Number of the entire imaging device smaller than thatof the lens system. According to some examples, the phase encoder can beconfigured to increase the smallest singular values of therepresentative matrix H of the lens system, thereby decreasing theCondition Number of the matrix of the entire imaging device (defined bythe lens system and the phase encoder).

As indicated above, the lens system may include a single lens, or one ormore lenses and possibly one or more other optical elements. It shouldbe understood that a phase encoder is constituted by appropriate one ormore phase-affecting transition regions associated with the lens system,so as to introduce a pre-determined phase correction to the imagingdevice to improve the condition of the corresponding matrix. In otherwords, the phase encoder may affect the phase of light incident thereonas compared to that outside the phase encoder (e.g. within the lensaperture), and possibly also differently affect the phase of lightportions passing through different regions within the phase encoder. Asa result, the entire imaging device creates a certain phase profile inlight passing therethrough.

Thus, for example, a phase encoder may be a stand-alone element (e.g.mask physically separated from the lens) located upstream or downstreamof the lens system, or located in between the elements of the lenssystem (this being possible as long as the light portions associatedwith the lens and the phase encoder are spatially separated). In anotherexample, the phase encoder may be configured as a phase-affectingelement attached to the lens of the lens system (e.g. a phase-affectingpatterned structure optically glued to the lens); or may be aphase-modifying element incorporated within the lens (generally withinone or more elements of the lens system). For example, the imaging lensmay be formed with a particular phase-pattern engraved on the lenssurface, in the form of a surface relief (profile) and/or materials ofdifferent refractive indices. Yet other possible examples of the phaseencoding element may include a mirrors structure in a reflecting mode ofoperation, a holographic phase encoding element, etc.

Reference is made to FIGS. 2A and 2B illustrating two specific but notlimiting examples of the configuration of imaging device 2 of thepresent invention. To facilitate understanding, the same referencenumbers will be used for identifying components that are common for allthe examples of the invention. In both examples, the imaging deviceincludes an imaging lens 10 (constituting a lens system) having anaperture LA, and a phase encoder 20, defining together an effectiveaperture EA. Also, in both examples the encoder is implementedsubstantially symmetrical with respect to the optical axis OA of thelens 10, although it should be understood that the invention is notlimited to this implementation, as will be described more specificallyfurther below. In these specific examples the phase encoder 20 occupiesthe periphery of the effective aperture EA of the imaging device. whilethe lens 10 is located in the central region thereof. It should howeverbe understood that the invention is not limited to such configuration.

In the example of FIG. 2A, phase encoder 20 surrounds the lens 10, beingin the form of a ring-like structure surrounding the lens 10. Generallyspeaking, this figure exemplifies the situation when the phase encoder20 does not overlap the lens 10 along the optical axis OA. The phaseencoder 20 includes one or more materials with a refractive index (orindices) different from that of the lens 10. Thus, the effectiveaperture EA of the imaging device 2 includes a first phase patternwithin a part R₂ of the effective aperture occupied by the encoder 20,and a remaining part R₁ of the effective aperture, in which lensingeffect is applied to light, is free of the first pattern (while may ormay not be formed with a second phase pattern).

In the example of FIG. 2B, phase encoder 20 is also in the form of aring-like element but overlapping with a part of the lens 10 within itsperiphery region. Thus, in this example, the effective aperture EA ofthe imaging device 2 is actually represented by (equal to) the lensaperture. Also, in this example the phase encoder 20 is implemented witha surface relief (profile) and may or may not be made of material(s)different from that of the lens. Here, similar to the above-describedexample, the phase encoder defines a first pattern within region R₂ ofthe effective aperture, leaving a remaining part R₁ of the effectiveaperture free of this pattern (and possible having another secondpattern).

The geometry of region R₂ and configuration of the first pattern thereinare selected such that the first phase correction function satisfies apredetermined condition with respect to a phase correction functioninduced by the lens region R₁ (generally, a remaining part of theeffective aperture). In some examples, this predetermined condition issuch that the value of the first phase correction function, all alongthe corresponding region R₂, is not smaller than the phase correctionfunction corresponding to region R₁; and in some examples is such thatthe value of the first correction function in region R₂ does not exceedthe other phase correction function corresponding to region R₁.

As will be shown further below, the geometry of said part of theeffective aperture carrying the first pattern, as well as theconfiguration of the first pattern itself, define a phase correctionfunction induced by the first pattern onto light passing therethrough.As for the second pattern that may be provided within an optical pathoverlapping with the remaining part of the effective aperture where thelens effect is applied, such a phase pattern may be aimed mainly atachieving another effect, e.g. extending the depth of focus. The use ofphase-masks in the optical path overlapping with the lensing region isgenerally known and therefore need not be described in details.

The following is an example of how to select the appropriateconfiguration for the phase encoder, namely the geometry (shape anddimensions) and the features of the pattern to be used in a specificimaging system.

A phase encoder may be designed to be incorporated in a lens system tomodify (typically, increase) a selected sub-group of the singular valuesof H, σ_(j) of the lens system matrix. For the sake of clarity andsimplicity of the description, σ_(i) denotes herein the whole group ofsingular values of matrix H, where i takes all the numbers from 1 to L,L being the size of H. Further, σ_(j) denotes said subgroup of singularvalues to be modified, and usually to be increased. Also, the positionsof the selected singular values σ_(j) in the matrix S will be denotedsimply “positions j” avoiding the need for more complex formalmathematical notations.

A diagonal matrix ΔS, in the size of S, may therefore be generated,having positive numbers in the selected positions j (corresponding tothe positions of the singular values of H which are to be modified). Ittherefore follows that the matrix sum of S and ΔS can yield aninvertible matrix with improved condition. In other words, if S1 is thesaid sumS1=S+ΔS  (11)then the matrix H1 is:H1=U·S1·V ^(t) =U·(S+ΔS)·V ^(t) =U·S·V ^(t) +U·ΔS·V ^(t) =H+U·ΔS·V ^(t)=H+O  (12)approximates a PSF matrix of the entire imaging device (lens andencoder), formed by the lens system represented by matrix H and thephase encoder represented by the additional part which is determined asfollowsO=U·ΔS·V ^(t).  (13)

In various embodiments, the two systems H and O are generally different,however observe the same object and their image is mixed. Therefore,these embodiments are referred to herein generally as parallel optics.

It should be understood that a construction of an actual phase encoder(mask) may be subject to additional constraints, on top of thoseexpressed in the formula (13) for O, as is further detailed below. Hencean actual phase encoder may be algebraically represented by a matrixapproximately equal to O but not necessarily identical to it.

The invented approach for designing the phase encoder utilizes selectingthe desired approximation for the phase encoder matrix resulting in theimage quality improvement. It should therefore be understood that Omatrix referred to hereinbelow as being that of the phase encoderrelates also to the desired approximation thereof.

Both the lens system and the phase encoder represented by matrices H andO “see” the same object and bore-sight to the same image plane. However,the lensing effect and the phase encoding effect are applied todifferent portion of light incident onto the effective aperture, thuspresenting a parallel-type optical processing. This effect can be takeninto consideration by dividing a lens area into two parts (or more ingeneral). The division can be done for example by dividing an extendedlens to a “lens zone” in the center and a “phase mask” in the lens rimzone (periphery), termed herein “Rim-ring zone”. The linear nature ofdiffraction allows such a separation, which is a division of theintegration into two zones. The integration boundaries for the “lenszone” are like those of inner circular pupil function, while theintegration boundaries for the “Rim-ring zone” are like those of annularpupil function. In simulation, those are continuous pupil functions withtwo different zones for two different phases.

It should be noted that under this approach the effective aperture canbe divided into as many zones (parts of different patterns) as needed.These zones can have arbitrary shapes and transmission values toappropriately affect the phase of light (and possibly also amplitude)passing through these zones.

Following the above discussion, the matrices H and O generate incoherentimpulse response for each field point, the coherent response will be ofthe form as follows (see for example [3]):{tilde over (h)}(x _(img) ,y _(img))=∫∫{circumflex over (P)}(λS _(img){tilde over (x)},λS _(img) {tilde over (y)})·exp(−j2π(x _(img) {tildeover (x)},y _(img) {tilde over (y)}))d{tilde over (x)}d{tilde over(y)}  (14)({tilde over (x)}=xp/λ·S _(img) ,{tilde over (y)}=yp/λ·S _(img))  (15)where (x_(img),y_(img)) are the image point coordinate, S_(img) is theimage distance. The explicit form of the pupil function is:{circumflex over (P)}(λS _(img) {tilde over (x)},λS _(img) {tilde over(y)})=P(λS _(img) {tilde over (x)},λS _(img) {tilde over(y)})·exp(jKW(λS _(img) {tilde over (x)},λS _(img) {tilde over(y)}))  (16)

Here, P( ) is the amplitude which is effected by local transmittance,and KW( ) is the phase effected by both lens aberration and phasedelements (the latter affecting the “rim-ring zone” only). The systemimpulse response is a super-position of the two “optics” responses,which is then also a function of its power which reflects both in thecross-action area (A) and in the transparency (T):{tilde over (h)} _(tot)(x _(img) ,y _(img))={tilde over (h)} _(lens)(x_(img) ,y _(img) ,A _(lens) ,T _(lens))+{tilde over (h)} _(rim-ring)(x_(img) ,y _(img) ,A _(rim-ring) ,T _(rim- ring))   (17)

However, in regular photography, the optical system describes imaging inincoherent illumination. Hence the matrix columns are the system PSF.Thus:

$\begin{matrix}\begin{matrix}{{{PSF}\left( {x_{img},y_{img}} \right)} = {{{{\overset{\sim}{h}}_{tot}\left( {x_{img},y_{img}} \right)}}^{2} =}} \\{= {{{\overset{\sim}{h}}_{lens}}^{2} + {{\overset{\sim}{h}}_{{rim} - {ring}}}^{2} +}} \\{{{{\overset{\sim}{h}}_{lens}^{*}{\overset{\sim}{h}}_{{rim} - {ring}}} + {{\overset{\sim}{h}}_{lens}{\overset{\sim}{h}}_{{rim} - {ring}}^{*}}} \approx} \\{\approx {{{\overset{\sim}{h}}_{lens}}^{2} + {{{\overset{\sim}{h}}_{{rim} - {ring}}}^{2}.}}}\end{matrix} & (18)\end{matrix}$

In that case, there are potential cross-products between the “lens” andthe “rim-ring” and therefore their contributions are not entirelyparallel as in field response. Theoretically, the cross products damagethe parallelism assumption. However, the nature of the problem is thatthe “rim-ring” PSF and “lens” PSF work in different zones of the FOV.Thus, the cross-products' power is relatively low so the parallelismassumption is generally reasonable.

Thus, according to some embodiments of the invention, a phase encoderthat induces a desired phase correction and thereby improves thecondition of the matrix S (reduces the Condition Number), may beconfigured in accord with the eigenvalues σ_(j) defined above and theresulting ΔS. An initial condition for the construction of the phaseencoder can be set as follows: For each singular value σ_(i), thereexists an eigen matrix M_(i) defined by the outer product of theappropriate column vectors u and v as defined in (6) and (7):M _(L×L) ^(i) =u _(i) ·v _(i) ^(t).  (19)Thus, the PSF matrix of the required phase encoder can be composed as alinear combination of the eigen matrices associated with the singularvalues σ_(j)—referred to as matrices M_(j)—where the actual coefficientsin the linear combination are the new, modified singular values. Adirect sum of the matrices M_(j) corresponding to the singular valuesσ_(j) (corresponding to the desired O) as is defined in (20) below, istermed herein Binary Matrix Spot Diagram (BMSD):

$\begin{matrix}{{B\; M\; S\; D} = {\sum\limits_{j}{M_{j}.}}} & (20)\end{matrix}$

The BMSD is thus used as an initial condition for the construction ofthe phase encoder O.

It should be noted that since the phase encoder is a physical entity,its algebraic representation must contain only real PSF for each fieldpoint. Further, in order to actually effect a change in the condition ofthe entire lens system represented by matrix H, the phase encoder shouldbe selected such that the modified system matrix H1 (that of the lenssystem and encoder) is invertible.

As a result of these constraints, construction of a phase encoder fromthe matrix O usually involves some necessary approximations. Since onthe one hand the phase mask is common for all field points, and on theother hand said linear combination of the eigen-matrices M_(j) is spacevariant, to yield a common best pupil function some compromise betweenbest pupil function of each field point must be made.

Reference is made to FIG. 3A which shows an imaging device 2 accordingto an embodiment of the present invention. According to this embodiment,imaging device 2 includes an imaging lens 10, and phase encoder 20 onthe rim (periphery) of the lens 10. The phase encoder 20 has arotational symmetry around the lens optical axis. Further, the phaseencoder 20 is characterized by two geometrical parameters: a radius(width) Δr and a shape function (surface profile) F. The imaging deviceis further characterized by a ratio between the transmission T_(lens) ofthe lens 10 and that T_(rim-ring) of the encoding element 20,T_(lens)/T_(rim-ring). The geometrical parameters Δr and F, and therelative transmission factor T_(lens)/T_(ring) are thus calculated asdescribed below in accordance with the singular values σ_(j) and thematrix ΔS so as to obtain a required improvement of the imaging lens 10.

The overall power Power_(ring) that reaches the image plane from thephase encoder relative to the power Power_(lens) that reaches from thelens is determined by the area of the encoder (that of part R₂ of theeffective aperture) relative to the area of the lens part R₁, and by therelative transmission factors. Assuming that the ring transmissionT_(ring) equals 1,

$\begin{matrix}{{PR} = {\frac{{Power}_{ring}}{{Power}_{lens}}\underset{D\operatorname{>>}{\Delta\; r}}{\approx}\frac{{2 \cdot \Delta}\; r}{T_{lens}^{2} \cdot {D/2}}}} & (21)\end{matrix}$and thereforeΔr=¼·T _(lens) ² ·D·PR  (22)

Referring back to FIG. 3A, β is the angle of intersection between a ray12 from the middle of the rim-ring zone R₂ and the optical axis in theimage plane:

$\begin{matrix}{{\tan(\beta)} = \frac{0.5 \cdot \left( {D + {\Delta\; r}} \right)}{Si}} & (23)\end{matrix}$where D is the dimension (e.g. diameter) of the lens region R₁.

It also follows from FIG. 3A that:R=Si/cos(β)  (24)

The ray aberration Rim, acting as the blur radius, is related to thewavefront derivative. For the maximal ray aberration one obtains:

$\begin{matrix}{\left. {{- \frac{R}{n}} \cdot \frac{\partial{W\left( {{xp}_{rim},{yp}_{rim}} \right)}}{\partial r}} \right|_{\max} = {{Rim} \cdot {\cos(\beta)}}} & (25)\end{matrix}$where W is the wavefront (the wavefront being related to the wave phasethrough phase=K*W were K is the wave number), (xp_(rim),yp_(rim)) arethe coordinates of the ring 20, and n is a refraction index of thesurroundings of the device in the optical path of light propagationthrough the imaging system. It follows that:

$\begin{matrix}{\left. {- \frac{\partial{W\left( {{xp}_{rim},{yp}_{rim}} \right)}}{\partial r}} \right|_{\max} = {\frac{{Rim} \cdot {\cos(\beta)}}{R} = \frac{{Rim} \cdot {\cos^{2}(\beta)}}{Si}}} & (26)\end{matrix}$

Assuming quadratic form of the shape function F, the phase can beexpressed as:

$\begin{matrix}{{{W\left( {{xp}_{rim},{yp}_{rim}} \right)} = {{F_{0}\frac{\left( {{xp}_{rim}^{2} + {yp}_{rim}^{2}} \right)}{\Delta\; r^{2}}} = {F_{0}\frac{\Delta\; r^{2}}{\Delta\; r_{\max}^{2}}}}},} & (27)\end{matrix}$where F0 is a constant. Differentiating (27) obtains the ray aberration:

$\begin{matrix}{{{- \frac{\partial{W\left( {{xp}_{rim},{yp}_{rim}} \right)}}{\partial r}} = {2 \cdot F_{0} \cdot \frac{\Delta\; r}{\Delta\; r_{\max}^{2}}}},} & (28)\end{matrix}$and applying the maximum value obtains:

$\begin{matrix}{{{- \frac{\partial{W\left( {{xp}_{rim},{yp}_{rim}} \right)}}{\partial r}}}_{\max} = {2 \cdot F_{0} \cdot {\frac{\Delta\; r_{\max}}{\Delta\; r_{\max}^{2}}.}}} & (29)\end{matrix}$From combining (26) and (29) the relation between the geometricalparameter F₀ and the blur radius is obtained:

$\begin{matrix}{\frac{2 \cdot F_{0}}{\Delta\; r_{\max}} = \frac{{Rim} \cdot {\cos^{2}(\beta)}}{Si}} & (30) \\{F_{0} = {\left. \frac{{{Rim} \cdot \Delta}\;{r \cdot {\cos^{2}(\beta)}}}{2 \cdot {Si}}\Leftrightarrow{Rim} \right. = \frac{2 \cdot F_{0} \cdot {Si}}{\Delta\;{r_{\max} \cdot {\cos^{2}(\beta)}}}}} & (31)\end{matrix}$

For example, the remaining part R₁ of the effective aperture associatedwith the imaging lens 10 may have a diameter D of 0.4 mm, and a distanceSi (roughly equal to the focal length f) from the imaging device to theimage plane is Si=0.69 mm. The imaging lens 10 is further characterizedby relatively high aberrations, having the Siedel sums S1=0.0123,S2=0.0130, S3=0.0199, S4=2.922·10⁻⁴ and S5=3.335·10⁻⁵. The exit pupil ofthe lens system is in the lens plane. The imaging system utilizing suchimaging device 2 is further associated with a square FOV having 10×10pixels and a width of 0.113 mm. The optical characteristics of theimaging system can be calculated using an optical simulation tool, andPSF matrix corresponding to the imaging system is also calculated.Further, a singular value decomposition (SVD) of the PSF matrix isobtained.

FIG. 3B shows 100 singular values of the obtained SVD matrix, and theinsert 350 shows in detail the smallest singular values. Accordingly,the Condition Number of the imaging lens system 10 is calculated to bek_((S))=σ₁/σ₁₀₀=87,640.

Next, in order to improve the system condition by reducing the conditionnumber of the imaging device used in the system, the system's weak (lowvalue) singular values are to be enlarged. Thus, a group of weaksingular values σ_(j) is selected to be modified. In this example thesix lowest singular values σ₉₅, . . . , σ₁₀₀ are selected to be modifiedfor the improvement of the lens system 10 performance. Accordingly, theeigen matrices M₉₅, . . . , M₁₀₀ as defined in (19) and their sum BMSDas defined in (20) are considered for the construction of the phasemask, as is further described below.

FIG. 3C shows the PSF shape of the BMSD after translating it back into a2D image. For convenience, only every fourth column is shown, thustwenty-five images 301, 302, . . . , 325 are presented. The majorobservation from the images 301-325 is that the point spreadcharacterizing the BMSD is shifted on the average by 4.5 pixels, with astandard deviation of 2 pixels, and is further distributed over the FOVwith a width (one standard deviation) of 3.1 pixels in x direction and2.0 pixels in y direction. This sets the required blur radius Rim as isfurther described below.

Referring back to FIG. 3A, the parameters Δr and T_(rim-ring) affect therelative power arriving to the image from the phase encoder 20, and theparameter F₀ is set in accord with Δr and with the required blur radiusRim, according to (31) above. The blur radius which is required from thephase encoding element 20 is selected to be roughly equal to the FOVsize namely Rim=0.113 mm. In order to set the values of these parametersan optimization process is performed, as is described below.

A preliminary set of parameters values is selected for the phaseencoding element 20, and a new PSF matrix is found for the imagingdevice 2. From the new PSF matrix a new SVD matrix is calculated and anew condition number is obtained from the new SVD matrix. This completesa first iteration of the optimization process.

A second iteration starts with a selection of a second set of parametersvalues for Δr, F₀ and T_(ring). A new condition number is re-calculatedfrom the new SVD matrix obtained in the second iteration, and iscompared to the new condition number obtained in the first iteration.This iterative process is repeated until a minimum, or, alternatively, asatisfactory low level, of the new condition number is achieved.

The resulting parameters values of the phase encoding element 20 of theimaging device 2 in this example, under an assumption that theaberration coefficients of the system are the same with or without therim, are Δr=0.03 mm, F₀=2.235 um and T_(ring)=0.9. The new conditionnumber associated with the imaging device 2 is found to be 2291,representing an improvement by a factor of about thirty-eight withrespect to the condition number of the lens 10.

In another example the resulting parameters of the phase encodingelement 20 are Δr=0.03 mm, F₀=1.56 um and T_(ring)=0.9. This exampleincludes an aberration model where the aberrations coefficients grow dueto the extension of system diameter associated with the rim. Theresulting condition number was found to be 3187. This condition numberrepresents an improvement by factor of about twenty-eight with respectto the condition number of the lens 10.

The image enhancement expressed in the improved image quality obtainedby the entire imaging device 2 compared to the lens 10 can bequantitatively described by a Mean Square Error Improvement Factor(MSEIF) [5] defined in (32):

$\begin{matrix}{{M\; S\; E\; I\; F} = {20 \cdot {\log_{e}\left( \frac{{{i^{image} - i^{Object}}}_{2}}{{{i^{restored} - i^{Object}}}_{2}} \right)}}} & (32)\end{matrix}$wherein i is the vector column representation (L×1) of matrix in thesize (N×M) corresponding to the number of pixels (N×M) of the image FOV,and comprising the grey-scale level of the pixels.

Thus, i^(object) represents an ideal image of the object, i^(Image)corresponds to an image obtained from the optical system beforerestoration (for proper comparison that was taken from the uncorrectedoptical system), and i^(Restored) corresponds to an image obtained afterrestoration. The operation ∥x∥₂ stands for the norm of the resultingvector x. An important limit is when the nominator and denominator normare equal (corresponding to MSEIF=0 db). At that point the restoredimage i^(Restored) and the image before restoration i^(Image) have equalsimilarity to the object, i.e. there is no use in restoration. MSEIFhaving a negative value indicates the case when the image restoration isworse than the image. The restored image in this example is provided bya simple matrix inversion according to (2) namelyi^(Restored)=H1⁻¹·i^(Image), where the i's denote the vector columns ofthe respective images and H1 is the PSF matrix of the imaging device 2,as defined in (12).

In the description given below, comparisons of image quality obtainablefrom conventional imaging systems (with no phase encoder based imagingdevice) and that of the invention are presented by simulations.

Reference is made to FIGS. 4A to 4D exemplifying the improvement inSignal to Noise Ratio (SNR) and in images obtained using a standardimaging system and an imaging system configured according to the presentinvention with the above described two aberrations models. FIGS. 4A and4C show Mean Square Error Improvement Factor (EMSIF) and FIGS. 4B and 4Dshow images obtained with corresponding imaging systems.

FIGS. 4A and 4C show graphically the Mean Square Error ImprovementFactor (EMSIF) as a function of Signal to Noise Ratio (SNR) in twooptical systems. In both figures graphs G1 correspond to those of theimaging system of the present invention and graphs G2—to theconventional imaging system (with no phase encoder). The graphs G2 andG1 show the MSEIF of imaging lens and imaging device of the inventionfor varying levels of Signal-to-Noise Ratio (SNR). It is shown that forany SNR level, the MSEIF of the imaging device of the invention issuperior to that of the conventional imaging system, and that theimaging device of the invention crosses the 0 db limit in SNR levelswhich are typically 20-37 db lower than that of the conventional optics,namely, it presents higher immunity to noise.

FIGS. 4B and 4D show images obtained with the imaging systems describedabove by graphs G1 and G2 in FIGS. 4A and 4C respectively, at twodifferent SNR levels. In both figures, images referenced by referencenumber 410 correspond to the ideal image of an object; 412—to theobject's image obtained by the conventional system (graph G2) at SNR=60db; and 414—to the same image 412 after restoration according to eq.(2). Further, “416” corresponds to the object's image obtained from theimaging system of the invention (graph G1) under the same SNR of 60 db,and 418—to the same image after restoration. Likewise, images 422 and424 are obtained from the conventional system at SNR level of 55 db,before and after restoration, respectively, and images 426 and 428 areobtained from the invented imaging system, under similar conditions,before and after restoration, respectively. These images demonstratethat under the same SNR-level conditions the improved imaging device ofthe invention combined with a restoration process (which will bedescribed more specifically further below) can obtain better images,than those obtainable using conventional imaging and processingtechniques.

Reference is now made to FIGS. 5A to 5D, which show another examples ofthe image enhancement provided by the imaging system of the invention ascompared to the conventional ones. FIGS. 5A and 5C show exemplary imagesobtained from such two imaging systems at an SNR of 65 db, where in bothfigures image 510 corresponds to an ideal image of the object, image 512was obtained from the conventional imaging device and image 514 is theresult of restoration (according to eq. (2)) of image 512 data, image516 is an image of the object obtained from the invented imaging device,and image 518 is the result of appropriate restoration of this imagedata. It is clearly visible that while 512 has remote resemblance to theobject 510, restoration (image 514) only degrades the result. Incontrast, the image 518 obtained from the invented system afterrestoration shows the best resemblance to the object 510.

FIGS. 5B and 5D show a further illustration of the comparison betweenthe conventional and invented systems. More specifically, the MSEIF ofthese two systems as a function of the SNR is shown. As shown, for agiven level of MSEIF, invented technique can tolerate a higher level ofnoise of about 20 db to 25 db.

FIGS. 6A to 6D show yet further examples of comparison between theconventional and invented techniques. FIGS. 6A and 6C show exemplaryimages obtained from the two optical systems at an SNR of 60 db, whereimages 610 represent ideal images of the object, 612 are images obtainedfrom the conventional system (graph G2 in FIGS. 6B and 6D), and 614 arethe images 612 after restoration (according to eq. (2)), 616 are imagesof the object obtained from the invented system (graph G1 in FIG. 6B),and 618 are the image results after restoration. It is clearly visiblethat the invented system creates an image which resembles the object,while the conventional system does not, with or without thereconstruction step.

As also shown in FIGS. 6B and 6D, for a given level of MSEIF, theinvented system can tolerate a higher level of noise of about 25 db.

Referring to FIGS. 7A to 7C, yet another example of comparison resultsis provided. In this example, the conventional imaging system ischaracterized by relatively low space variant aberrations, having theSeidel sums S1=0.0123, S2=2.59*10⁻⁴, S3=3.97*10⁻⁴, S4=2.922*10⁻⁴ andS5=3.335*10⁻⁵. Further, the systems (conventional and invented ones) areconfigured to have de-focus of 0.176 mm. Thus, this example illustratesthe improvement obtained by the phase coding imaging device of theinvention in a case of a dominant space-invariant blur, in the form ofde-focus.

More specifically, FIG. 7A shows three graphs, 71, 72 and 73, of thesingular values of the systems, where graph 71 corresponds to thesingular values of the conventional imaging system in an in-focusconfiguration, having a condition number of 3.9; graph 73 shows thesingular values of the same system in the de-focus arrangement thereof,with a condition number of 6413; and graph 72 shows the eigenvalues ofthe imaging device of the present invention 2 in the de-focusedposition, having a condition number of about 97. Thus the improvement inthe condition number in the invented system with respect to theconventional one is about 66. It should be understood that here theconventional system is considered that having an imaging device formedby the single imaging lens, and the invented system is that in which theimaging device has the same imaging lens and a phase encoder.

FIG. 7B compares the MSEIF of the systems as a function of the SNR(graph G1 corresponds to the invented system and graph G2—to theconventional system), and shows that for a given level of MSEIF,invented system can tolerate a higher level of noise of about 37 db,with respect to the conventional system.

FIG. 7C shows exemplary images obtained from the conventional andinvented systems at an SNR of 45 db. Here, 710 is an ideal image of theobject, 712 is an image obtained from the conventional system, and 714is the image 712 after restoration (according to eq. (2)); 716 is animage of the object obtained from the invented system, and 718 is theimage result after restoration. It is clearly visible that the inventedtechnique provides an image which resembles the object, while theconventional technique does not, with or without the reconstructionstep.

Referring to FIG. 8, there is illustrated, by way of a flow diagram, anexample of a method of designing (constructing) a phase encoderaccording to the invention. According to this example, the phase changeP(x,y) induced by the phase encoder is determined in every point (x,y)on the phase encoding element plane.

A PSF matrix H of the imaging lens arrangement (i.e. in the absence ofthe phase encoder) is obtained (step 801), from experimentalmeasurements using specific imaging lens arrangement (e.g. of certainlens diameter, curvatures, material, thickness, refraction index, aswell as object and image distances), or simulating the same for thisimaging lens arrangement, or supplied by manufacturer. Then, an SVD formS,V,U of the matrix H is calculated (step 802). A group of weak singularvalues of H, σ_(j), is selected (step 803) and a new matrix ΔS withimproved singular values is constructed (step 804).

In the PSF matrix ΔH (corresponding to the matrix ΔS according to (12)),each column represents the PSF of a different field point. Hence, thecolumns are converted to 2D PSF image and a representative PSF is chosen(step 805). Next, the phase at the exit pupil of the system (of theimaging lens device is calculated (step 806) for a selected number offield points. In order to obtain a physical result for the calculatedphase, for each field point using Fourier transform and inverse Fouriertransform relation between image plane and exit pupil plane a“Ping-Pong” iterative process [5] is carried out (step 806) untilconvergence of the phase function at the system pupil exit is obtained.This process is repeated (step 807) for all the selected field points(m,n) until a set of phase functions, P_(m,n)(x,y) (one associated witheach field point) is obtained. Generally, these phase functions differfrom one another, thus there cannot actually be a real phase encodercapable of inducing all of them. Therefore, selecting or generating onerepresentative phase function from the group of phase functions, isrequired.

According to some embodiments, a cross-correlation matrix ρ_(m,n) isthus calculated (step 808) between all phase functions, and the phasefunction {circumflex over (P)}(x,y) characterized by having the bestaccumulated cross-correlation

$\sum\limits_{m \neq n}^{N}\left( \rho_{m,n} \right)^{2}$to all other phase functions is selected (step 810).

Considering the entire imaging device (lens arrangement and phaseencoder) inducing a phase correction {circumflex over (P)}(x,y) at theexit pupil of the device, the PSF matrix is calculated again, and theassociated condition number is obtained (step 811). This completes asingle iterative step of the phase mask construction. If the conditionnumber is not satisfactory (step 812), the above described sequence ofoperations is repeated (step 812). The common correction phase{circumflex over (P)}(x,y) is taken as the initial condition of theiterative process for all selected field points, repeating steps(806)-(812). The iterative process thus repeats until a satisfactoryimprovement in the condition number of the PSF matrix of the improvedimaging device (optimal phase encoder) is obtained (step 813).

Reference is made to FIG. 9A to 9F, exemplifying the use of the methoddescribed above with reference to FIG. 8 for constructing a phaseencoder to be used with an imaging lens and arranged with respect to thelens similar to examples of either one of FIG. 2A or FIG. 2B, namely thephase encoder located within the periphery region R₂ of the effectiveaperture surrounding the lens region R₁. The remaining part R₁ of theeffective aperture associated with the imaging lens 10 has a diameter Dof 0.4 mm; and a distance Si from the imaging device (from its exitpupil) to the image plane is Si=0.69 mm. The imaging lens 10 ischaracterized by the Siedel sums S1=0.0123, S2=0.0130, S3=0.0199,S4=2.922*10⁻⁴ and S5=3.335*10⁻⁵. The imaging system has a square FOVhaving 10×10 pixels and a width of 0.113 mm.

FIGS. 9A and 9D show two examples of the progress of the conditionnumber being an image restoration parameter (on an arbitrary scale) ofthe imaging device formed by lens and phase encoder, as a function ofthe iteration number through the iterative process. The best conditionnumber (the lowest) was found to be about 1815 in point A on the graphin FIG. 9A based on an aberration model assuming the same aberrationcoefficients for both the systems with and without the rim, and about2476 in point A of the graph in FIG. 9D based on the aberration modelassuming that aberrations coefficients grow due to the extension ofsystem diameter which associate with the rim. This corresponds to animprovement by a factor of about 48 or 35.4 with respect to thecondition number of the imaging lens for FIGS. 9A and 9D respectively.FIGS. 9B to 9C and FIGS. 9E to 9F show the corresponding phasecorrection function induced by the phase coder: FIGS. 9B and 9E showtopographical maps of the phase correction on the periphery zone R₂; andFIGS. 9C and 9F show the same phase correction in a 3D histograms.

Reference is now made to FIGS. 10A to 10D, which show two examples ofthe image enhancement provided by the imaging device of the presentinvention as compared to the conventional device. FIGS. 10A and 10C showexemplary images obtained by two systems, one being the conventionalsystem and the other being the invented system implemented using theprinciples illustrated in FIGS. 9B-9C; both at an SNR of 60 db.

Each of FIGS. 10B and 10D show two graphs G₂ and G₁ comparing the MeanSquare Error Improvement Factor (EMSIF) as a function of Signal to NoiseRatio (SNR) in the two optical systems corresponding to FIGS. 10A and10C respectively.

In FIGS. 10A and 10C, 1010 are ideal images of the object, 1012 areimages obtained from the conventional system and 1014 are images 1012after restoration (according to eq. (2)); 1016 are images of the objectobtained from the system of the invention, and 1018 are the result afterrestoration. Images 1012 and 1016 have poor resemblance to the object1010. The restored images 1014 obtained from the conventional systemdegrades the result. In contrast, images 1018 obtained from system ofthe present invention shows the best resemblance to the object 1010.

As shown in FIGS. 10B and 10D, for a given level of MSEIF, the inventedsystem can tolerate a higher level of noise of about 25 db or about 20db (for the examples of FIGS. 10B and 10D respectively), as compared tothe conventional system.

It should generally be noted that the reconstruction step of the imageis not limited to the process of inverting matrix H1 as is described inthe examples above.

According to some other embodiments of the invention, imagereconstruction can be done by any known method that is intended toremove the phase encoding introduced by the phase encoder to the imagewhile being created by the imaging device, thereby removing also atleast some of the image blur. For example, the image reconstructionmethod can be provided by least square. According to the least squaremethod, the reconstructed image i^(restored) is obtained by minimizingthe norm E in (33):E=∥H1·i ^(restored) −i ^(restored)∥₂  (33)where i^(Image) is the image column vector associated with the PSFmatrix H1 (namely, the image obtained from the imaging device beforereconstruction).

According to another example, the so-called regularization method, theimage i^(restored) may be obtained by minimizing E in (34):E=∥H1·i ^(restored) −i∥ ₂ +α∥I·i ^(restored)∥₂  (34)where α is a constant (usually 0<α<1) and I is the unity matrix. Suchregularizationi ^(restored)=(H1^(t) ·H1+α·I)⁻¹ ·H1·i  (35)where I is the identity matrix, α is the regularization factor. Forsimulations bellow, it was assumed that α=1/SNR. This method will beexemplified more specifically further below.

The following is the description of another example of the technique ofthe invention. An imaging system captures images with a specific FOV.The BMSD matrix can be approximately decomposed by series oftransmission or trajectories matrices which are associated with shiftingof the captured image relative to the imaging FOV, i.e. some of theimage will get out of the FOV thus the transformation matrix between theobject space and image space will be changed. This technique is at timereferred to herein below as shifted lenses technique or shifted imagestechnique or transformation technique or trajectories technique.

Assuming a pixel confined imaging system is used, its transformation(PSF) matrix from object space to image space is a unit matrix.Following the lexicographic order of the vectors, the mapping of theobject coordinates (l_(in)) to the image coordinates (l_(out)) is:(l _(in))→(l _(out))=(m−1)·N _(n) +n→(m−1)·N _(n) +n  (36)

By determining a PSF matrix, the field of view (FOV) of the system isdefined. Assuming, a finite image is captured, if the image is shiftedby (Δm,Δn) relative to the fixed FOV origin, the mapping of the objectcoordinates (l_(in)) to the image coordinates (l_(out)) will change to:(l _(in))→(l _(out))=(m−1)·N _(n) +n→(m+Δm−1)·N _(n) +n+Δn  (37)

With a series of shifts, a series of transfer matrices is created, eachof which “draws” a different “Trajectory” over the 2D L×L empty matrix.

Referring to FIG. 14, there is shown a relation between the image shiftand the trajectories matrix in a 6×6 matrix. It is shown that when theFOV is fixed, there is a duality between the image shift and theInput/Output matrix representation. For each shift, another transfermatrix designated as O_(l) is obtained.

To realize an auxiliary system with the required auxiliary PSF matrix O,the FOV is defined as the H matrix FOV and decompose O by a series ofweighted shifted “transformation” matrix as follows:BMSD≈O=Σ _(i=1) ^(M) W _(l) ·O _(l)=Σ_(i=1) ^(N) Ŏ _(l)  (38)

For each “transformation”, there is a different weight constant (W_(l)).The weight is defined by solving the following average equation:

$\begin{matrix}{W_{i} = {\frac{1}{N_{l}}{\sum\limits_{i = 1}^{L}{\sum\limits_{j = 1}^{L}{B\; M\; S\;{{D\left( {i,j} \right)} \cdot {O_{l}\left( {i,{j;{\Delta\; m}},{\Delta\; n}} \right)}}}}}}} & (39)\end{matrix}$where N_(l) is the number of instances in the specific “Trajectory”,BMSD (see eq. (20) above) is the target PSF matrix for the auxiliarysystem O, ∘ is the projection operator which serves to project the BMSDover O_(l). Generalizing equations (38) and (39), the sum of alltransformation matrices O_(l) can be used to approximate the BMSD (seeeq. (20) above) of the lens 10. By minimizing the square difference E in(40), an approximation to the BMSD can be optimized.

$\begin{matrix}\left. {{E = \left( {{B\; M\; S\; D} - {\sum\limits_{l}{W_{l}O_{l}}}} \right)^{2}}}\rightarrow{\min.} \right. & (40)\end{matrix}$

Finally the overall system response is:

$\begin{matrix}{{H\; 1} = {H + {\sum\limits_{l}{W_{l}{O_{l}.}}}}} & (41)\end{matrix}$Reference is now made to FIGS. 11A and 11B together, showing yet furtherexample of an imaging device 2 of the present invention. The imagingdevice 2 includes an imaging lens 10, and a phase encoder 20. In thisexample, the phase encoder 20 has a pattern (constituting a firstpattern) in the form of an array of spaced-apart lenses 22 (theso-called “shifted lenses”). As also shown in the figure, the phaseencoder may optionally include folding elements 30, capable of affectingthe phase of light thus redirecting light beams. These may for examplebe prisms, beam splitters (not shown) etc. It should be understood thatshifted lens 22 can be implemented by more than one lens, for examplebeing an assembly of three lenses 24, as is exemplified in FIG. 11B. Theimaging device 2 may potentially further include field stops 40 fordefining the FOV of the lenses 22, the field stops being incorporatedfor example as part of the lens assembly 24, as is shown in FIG. 11B.

It should also be noted that although the “shifted lenses” embodiment isexemplified above in the effective aperture configuration of FIG. 2A,the same concept can be implemented in the configuration of FIG. 2B.

As further shown in FIGS. 11A and 11B, according to this embodiment, theshifted lenses 22 have their optical axes parallel to the optical axisof the imaging lens 10 and they form a plurality of images of the object(“shifted images”) on the image plane of the optical system. Theseshifted images contribute to strengthening the weak parts of the PSFmatrix of the imaging lens 10. Each shifted lens 22 can be representedby a PSF matrix O_(l) (k=1, . . . , N, N being the number of shiftedlenses) in the coordinate system of the imaging lens 10. For example,selecting the number of shifted images (corresponding to the number ofshifted lenses) and the amount of shift (distance between the shiftedlenses) may be used to improve the approximation. Additionally oralternatively, selecting proper transmission factors, represented byweight constants W_(l), can further improve the approximation.

According to yet further embodiment of the present invention, the“shifted images” approach for improving matrix condition of an imagingdevice can be implemented by an image processing algorithm used with animaging technique (not necessarily utilizing a phase encoder as aphysical element). Such imaging technique utilizes creation of twoimages with different Numerical Apertures of light collection anddifferent levels of blur. The system may include two separate imagingdevices referred to above as the main system and the auxiliary system.The main and auxiliary systems observe the same field of view, which forexample might be similar to the image capture used in phase diversitysystems. The principles of phase diversity technique are known per seand need be described in details, except to note that it is aimed atreducing the amount of data to be processed. An image is the combinationof two unknown quantities, the object and the aberrations; theaberrations are perturbed with a known difference in phase between twosimultaneously collected images, for example by recording one of theimages slightly out of focus [6-10].

Assuming an ill conditioned main system, the above target BMSD can bedefined and decomposed by digital summation of N shifted and weightedreproductions of the auxiliary system image. Finally, the main systemimage is added to that summation, a new image associated with theimproved condition system is obtained. The governing equations (35)-(39)are applicable in this process, while the shift and weight are donedigitally on a captured auxiliary system image.

According to some embodiments, a desired set of weight factors can beobtained for example by minimizing E in (39). Further, the weightfactors W_(l), in (35)-(39) may assume both negative and positivevalues, since the summation is performed computationally and notphysically. Accordingly, a relatively good approximation of the BMSD canbe obtained by a proper summation of the matrices, and a considerableimprovement of matrix condition can be consequently achieved.

Reference is made to FIG. 12, showing an example of an imaging system 50utilizing the above concept of “shifted images”. Imaging system 9includes a first imaging device 52 having an optical axis OA₁ and aNumerical Aperture NA₁ and a second imaging device 54 having opticalaxes OA₂ parallel to the optical axis OA₁ and a lower Numerical ApertureNA₂. It should be understood that imaging devices 52 and 54 may beassociated with their own light detector or a common light detector.Also, imaging devices 52 and 54 irrespective of whether being parts of acommon integrated system or not, need not be operative concurrently;what is needed for this aspect of the invention is the provision of twodifferent image data pieces collected by such different imaging devices.Thus, as shown in the figure, imaging system 50 includes an imagedetector 56 and a control unit 6, configured and operable to registerand to process image data provided by detector 56. The first imagingdevice 52 can include for example a high Numerical Aperture (NA) lens,which generates a high intensity, but relatively blurred image. Thesecond imaging device 54 can include for example a low NA lens, whichprovides a relatively blur-free (sharp) image, with low intensity andrelatively low SNR. Both imaging device “see” the same object andbore-sight to the same image plane, therefore the images produced bythem are obtained from the same direction from the object. The images(i_(main)) and (i_(aux)) produced by the two imaging devices 52 and 54are detected by detector 56 and registered by the control unit 6independently from one another.

According to some embodiments, the weak parts of the first imagingdevice 52 are improved by adding to the image (i) a combination of Nshifted images (i_(l)) obtained by the second imaging device 54. Theimages (i_(l)) are obtained computationally from the auxiliary imagei_(aux) by duplications and shifts. For a given BMSD of the firstimaging device 52, a desired combination of the shifted images (i_(l)),

$i = {\sum\limits_{l = 1}^{N}{W_{l} \cdot i_{l}}}$is obtained. The sum (i_(main)+i) is then reconstructed—e.g. bymultiplication by an inverted matrix H₁ ⁻¹—to generate an enhanced imageof the object.

According to some examples of the invention, a desired set of weightfactors W can be obtained in the way described below. An algebraicrepresentation H of the first imaging device 52—e.g. a PSF matrix—isobtained and presented in a modal form—e.g. SVD. The weak parts of themodal matrix are identified and a BMSD matrix is generated accordingly,as is described above.

Reference is made to FIGS. 13A and 13B, which show an exemplaryimplementation of the above-described “computational” approach to the“shifted images” aspect of the invention. FIG. 13A compares imagesobtainable with the conventional technique and that of FIG. 12; and FIG.13B shows graphs H₁, H₂ and H₃ comparing pixels grey level of threeimages: ideal picture of the object (object itself—graph H₁) and imagesof FIG. 13A (graphs H₂ and H₃). The lens aperture (being the effectiveaperture in this case) of the imaging device 52 has a diameter D of 0.4mm, is distanced from an imaging detector a distance of Si=0.69 mm, andis characterized by relatively high aberrations, having the Siedel sumsS1=0.0123, 82=0.0130, 53=0.0199, S4=2.922*10⁻⁴ and S5=3.335*10⁻⁵. Thelens/effective aperture of the imaging device 54 has a diameter of 0.16mm, and generates a PSF approximately confined to an eleven micronsquare pixel. The FOVs of the imaging devices 52 and 54 are the same andare in the form of a square 10×10 pixels with 0.113 mm length side.

FIG. 13A shows exemplary image reconstruction at an SNR of 55 db firstdevice and 25 db for the second device. In the figure, 1310 is an idealimage of the object, 1312 is an image obtained from the first higher NAimaging device 52 and 1314 is the image 1312 after restoration(according to eq. (2)(2)), and 1316 is an image of the object obtainedfrom the second imaging device 54. Taking into account the low intensityof image 1316 it is multiplied by the ratio of the area of the firstlens 52 and the second lens 54, namely by (0.4/0.16)². The resultingimage is quite dark but relatively sharp.

Image 1317 is a weighted sum of a multitude of duplicate images of image1316, shifted with respect to one another by essentially all possibleshifts allowed in a 10×10 pixels FOV. The region of interest in image1317, marked by the rectangle in the center 1317A, is used for theimprovement of the system matrix condition further in the process. Image1318 is the direct sum of image 1312 of the first imaging device 52, andimage 1317A. Image 1319 is the result of image 1318 after restorationaccording to eq. (2).

It should be appreciated that the images 1312 and 1314, obtained fromthe imaging device 52 before and after restoration, respectively, haveboth poor resemblance to the object 1310. In contrast, the restoredimage 1319 obtained by the complete “shifted images” technique showsrelatively high resemblance to the object 1310.

FIG. 13B graphically compares between the conventional and inventedtechniques, i.e. between images 1314 and 1319. The grey level of eachpixel in the object image 1310 (in asterisks) is compared between therestored image 1314 of the imaging device 52 (triangles) and therestored image 1319 of the combination of devices 52 and 54 (circles).The graph shows that the pixels grey level in the restored image 1319follow closely the pixels grey level in the object image 1310. Incontrast, the pixels grey level in image 1314 have a very large spreadwith no observable correlation to the pixels grey level of the objectimage 1310.

Reference is made to FIGS. 13C and 13D, showing simulation results forthe above described technique. FIG. 13C exemplifies an object ensembleused in the simulations; and FIG. 13D compares the restoration averageMSEIF ensemble results as a function of the main system SNR for the“study case” restoration “as is” (i.e. imaging device having the mainlens system only, with no auxiliary lens) and the above-described“trajectories” or “shifted lenses” technique utilizing a simpleauxiliary lens. Here, graph G1 corresponds to the image restoration byregularization for the “trajectories” based system with simple auxiliarylens, graph G2 corresponds to the “study case” lens restoration bymatrix inversion, and graph G3 corresponds to the “study case” lensrestoration by regularization.

In the above described “shifted lenses” or “trajectories” examples, theauxiliary lens system was assumed to be of a so-called “perfect” or“pixel confined” configuration that does not induce an image blur. Itwas also emphasized that for realistic demonstration, the auxiliarysystem formed by a low NA simple lens which is almost pixel confined isto be considered. It should also be understood that the principles ofthe “trajectories” or “transformation” technique may be used with alinear system other than optical, for example a mechanical systemvibration measurement. In this connection, reference is made to FIG. 13Eshowing a system formed by main system 10 characterized by the mainsystem matrix H, and auxiliary system 20 characterized by the auxiliarysystem matrix O. Assuming both systems capture finite series of nsamples in time from n locations of the same vibrating system, if Hmatrix system is ill conditioned, a target matrix composed from itseigen matrixes equivalent to the above BMSD can be calculated, then thesample data of the auxiliary matrix system O can be shifted and weightedaccording to the Trajectories based decomposition transformationmatrixes L₁, L₂, . . . , L_(n). Finally, by summing the H matrix datawith the processed auxiliary data, an improved conditioned parallelsystem is obtained.

Turning back to the optical system design, the auxiliary system itselfmay have a blurred response. Moreover, the same lens system may functionas the “main system” and as the auxiliary system”, in which case the“main” and “auxiliary” functions are constituted by successive imageacquisition performed by the same lens system.

The following is an example of how to extend the “trajectories” or“transformation” model to a more realistic auxiliary lens with a blurredresponse (which will be at times referred to herein below as BlurredTrajectories or blurred transformations).

Extending the Trajectories or Transformations model to the general caseof a blurred auxiliary lens, the auxiliary lens image is not pixelconfined, hence relating it to the perfect object, it is first blurredand then weighted and shifted. In this case, the O matrix is decomposed(eq. (38) above) by “blurred transformation” calculated by simple matrixmultiplication.Ô1_(i)(i,j;Δm,Δn)=O _(l)(i,j;Δm,Δn)·H _(aux)(i,j)  (42)Following the “blurred transformation” approach, the normalizationfactor N is modified:

$\begin{matrix}{{\hat{N}}_{l} = {\sum\limits_{i = 1}^{L}{\sum\limits_{j = 1}^{L}{{O_{l}\left( {i,{j;{\Delta\; m}},{\Delta\; n}} \right)} \cdot {H_{aux}\left( {i,j} \right)}}}}} & (43)\end{matrix}$Finally, the weighting factor for eq. (37) is:

$\begin{matrix}{{\hat{W}}_{l} = {\frac{1}{{\hat{N}}_{l}}{\sum\limits_{i = 1}^{L}{\sum\limits_{j = 1}^{L}{B\; M\; S\;{{D\left( {i,j} \right)} \cdot \hat{O}}\; 1_{l}\left( {i,{j;{\Delta\; m}},{\Delta\; n}} \right)}}}}} & (44)\end{matrix}$The overall decomposition is:BMSD≈O=Σ _(i=1) ^(M) W̆ _(l) ·Ô1_(l)(i,j;Δm,Δn)  (45)

FIGS. 15A and 15B show the “transformation” matrices for the 6×6 PSFmatrix for 3×2 FOV. FIG. 15A presents the pixel confined case (assumingpixel confined auxiliary lens), and FIG. 15B presents the “blurredtransformation” with a blurred auxiliary lens assuming a 2×2 kernel(presented in the bottom right). Broadening the ideal “transformation”(FIG. 15A) to the “blurred transformation” (FIG. 15B) depends on theshape of the blurred auxiliary system kernel. This gives a degree offreedom to choose the blurred auxiliary system which can best decomposethe target BMSD.

For example, the main system matrix H may be as follows:

$\begin{matrix}{H = \begin{bmatrix}1.00 & 2.10 & 3.00 & 4.00 & 5.00 & 6.000 \\1.00 & 2.10 & 3.30 & 4.40 & 5.10 & 6.100 \\1.05 & 2.30 & 3.20 & 4.10 & 5.40 & 6.200 \\1.05 & 2.11 & 3.10 & 4.12 & 5.14 & 6.110 \\1.11 & 2.22 & 3.30 & 4.14 & 5.21 & 6.111 \\1.21 & 2.21 & 3.41 & 4.12 & 5.17 & 6.100\end{bmatrix}} & (45)\end{matrix}$

The matrix is ill conditioned with a condition number of κ=18625.

Using the above described “blurred transformation” technique, thesystem's matrix has 6 eigen matrices. For example, the last two eigenmatrices can be arbitrarily determined as the target BMSD. Under theassumption of pixel confinement, a set of 15 “transformations” isobtained presented in FIG. 15A. The resulted parallel optics system waswith an improved condition number of =72.6. For the same study case (themain system matrix H of (45)), following equations (42)-(44) above, andassuming a blurred auxiliary system, the resulted “blurredtransformation” is as in FIG. 15B and the parallel optics conditionnumber is κ=362. Although in the second case, there is some overlapbetween the “blurred transformations”, the set of the projections stillenables to decompose the target BMSD sufficiently.

The following are simulation results for the “blurred transformation”using two different optical systems: a space variant imaging system anda highly defocused imaging system. The performance of both systems wastested in image restoration over an ensemble of 8 objects (see FIG. 13C)under various SNR levels. The blurred images are subject to whiteGaussian additive noise.

The restoration performance is determined by the Mean Square ErrorImprovement Factor (MSEIF)—equation (32) also presented above:

$\begin{matrix}{{M\; S\; E\; I\; F} = {20 \cdot {\log_{e}\left( \frac{{{I_{L \times 1}^{image} - I_{L \times 1}^{object}}}_{2}}{{{I_{L \times 1}^{res} - I_{L \times 1}^{object}}}_{2}} \right)}}} & (45)\end{matrix}$In this function, the restoration error and the blur error are bothrelative to the ideal object (I^(object)). When MSIEF<0 db, the restoredimage (I^(res)) is worse than the blurred optical image (I^(img)), sothere is no use in performing restoration. For each SNR, a set of 2400measurements of the 8 objects was performed, and then the MSEIF wascalculated for each measurement and the average value over the ensemble.The results were presented as a function of the main system SNR.

Since the object range is limited to a dynamic range of 256 gray, it wasassumed that restoration gray levels below 0 and above 255 are out ofthe dynamic range, and hence they were rounded to 0 and 255respectively. For post processing, Following equation (35)regularization was used.Î _(L×1) ^(res)=(H1^(t) ·H1+α·I)⁻¹ ·H1·I _(L×1) ^(image)  (46)

For demonstration of the “blurred transformation” capabilities, it wasassumed that the auxiliary system used is blurred, and yields such badimages that if taken as restoration results the resulted MSEIF will benegative. Equation (47) presents the auxiliary system PSF:

$\begin{matrix}{{P\; S\; F} = {\frac{1}{11} \cdot \begin{bmatrix}1 & 1 & 1 \\1 & 3 & 1 \\1 & 1 & 1\end{bmatrix}}} & (47)\end{matrix}$

Considering the space variant ill conditioned imaging system, thesimilar simulation conditions were used, while removing the assumptionof a pixel confined auxiliary lens. The main system is generally similarto that described above with reference to FIG. 3B, being a highlyblurred space variant imaging system with a condition number of κ=87640.The main optical system is a 0.4 mm diameter lens. The image distance is0.69 mm and the field of view is ±4.673 deg. The system suffers fromstrong Seidel aberrations: S1=0.0123, S2=0.013, S3=0.0199, S4=2.92e−4,S5=3.335e−5. Image sizes 10×10, n=1. The FOV is asymmetric for −0.6pixel in x direction and +1.4 pixel in y direction.

The auxiliary lens is a 0.16 mm diameter lens with a space invariant PSFas in eq. (47). The auxiliary system works with a lower NA than the mainsystem. This effects the signal level and is reflected in a SNR level of16 db lower than that of the main system. In PSF calculation, adiffraction model was used, implemented using a 512×512 FFT operatormatrix. Regarding the NA condition, it should be noted that theinvention is not limited to any specific requirement for the NA of theauxiliary system at all and relative to that of the main system.

Since the “transformation” decomposition is of a diagonal form, a searchwas performed for an eigen matrices combination which yields a BMSD witha “Toeplitz” like shape. The search is done automatically, and in eachstep an additional eigen matrix was added and the condition number ofthe resulted parallel optics was calculated. The inventor have found fewlocal minima and chose the solution with a condition number of κ=1212.Since the ability to decompose the target BMSD with high fidelitydepends on both the choice of target BMSD and the auxiliary system PSFprofile, this is generally not an optimal solution of the system but alocal minima.

FIGS. 16A and 16B show restoration average MSEIF ensemble results,presenting two sets of measurements of the average MSEIF in foursystems. Graphs A1 corresponds to the system having the “main lens”without additional optics; graphs A2 corresponds to the quadratic“Rim-ring” parallel optics design described above, graphs A3 correspondsto the parallel optics using the above described “Blurred Trajectories”,all being restored by the regularization method described by eq. (46)above. Graphs A4 is a reference for the MSEIF value of the blurredauxiliary lens image which was used with the “Blurred Trajectories”method as if it was a restoration outcome. The negative MSEIF valuessignifies that it cannot serve as a restoration result as is. Asexpected, the results follow the matrix condition. For the “blurredTrajectories” (graph A3) with condition number of κ=1212, theimprovement in restoration quality (MSEIF value) is the best, followingis the quadratic “Rim-ring” (graph A2) with a condition number ofκ=2290.6 in the results shown in FIG. 16A including the assumption thatthe aberration coefficients for both the systems with and without therim are equal. And a condition number of κ=3187 in the results shown inFIG. 16B, assuming an accurate aberration model including growth of theaberrations coefficients due to the extension of system diameterassociated with the rim. Last is the graph A1 case without additionaloptics with a condition number of κ=87640. The regularization factor αincreases as the SNR decreases (eq. (46)). Due to the truncating natureof regularization, in this method as the SNR decreases the truncating isdeeper. Since the auxiliary system is composed of the system eigenmatrices, the outcome of this truncating is that the auxiliary lenscontribution is weaker until results asymptotically converge with theoriginal system solution. The “Blurred Trajectories” method provides animportant result where by mixing two blurred images an improvedrestoration is gained which for a wide range of SNR is better than therestoration results achieved by regularization only.

Reference is made to FIGS. 17A and 17B exemplifying typical images andrestorations by regularization (eq. (46)) of the above systems, all inmain SNR=45 [db], showing: the study case image (SC-Img.) withoutparallel optics; auxiliary lens image (Aux-Img), the object (Obj.), thestudy case restoration by regularization (SC-Res.), the quadraticRim-ring restoration (RR-Res.), and the Blur Trajectories restoration(Traj-Res.) in both figures. The mean improvements in the MSEIF is 4.4[db]. The visual results follow the mean MSEIF results. In general, therestoration performance with “blurred Trajectories” (Traj-Res.) is equalor better than those of the study case restoration without paralleloptics (SC-Res), and especially better for objects 3, 4, 7 and 8.Comparing the restoration appearance of the “Blur-Trajectories” withthat of the “Rim-ring” (RR-Res), the results are generally in the samelevel.

The following is an example of a deep defocused imaging system case. Inthis case, a standard 0.4 mm diameter double convex lens is used. It isassumed that a 0.69 mm focal distance monochrome imaging (λ=0.5875 μm)is performed. The lens material is SCHOTT K10. The system's FOV, pixelsize and image distance are the same as in the above described example(FOV is ±4.67 deg, pixel size is 11 μm). The lens is subject to nominalSeidel aberrations and to a 176 μm defocus. This is shown schematicallyin FIG. 18. The algebraic representation of this system (main system) isa 100×100 almost space invariant matrix with a condition number ofκ=6412.5. This matrix is the “main” system. As the auxiliary lens, thesame 0.16 mm diameter blurred system with the same PSF (eq. (47)) wasused.

First, the target BMSD was graphically determined. FIG. 19 shows the“main” system singular values graph and the chosen (target) BMSD matrixin this case. It is seen from the singular values graph that from the8^(th) singular value, the graph is monotonic and decreasing, so thedecision to choose the 30^(th) singular value and not a higher value asthe limit for the BMSD is not influenced by the matrix conditionreduction alone but from the BMSD shape. The resulted BMSD is a“Toeplitz” like matrix which is close in shape to the “Trajectories”“diagonal” shape so it can be decomposed by the latter adequately. Inthis case, the theoretical condition number is 65.2 and the resultedcondition number using the “blurred Trajectories” is κ=238.7.

FIG. 20 presents a comparison between the mean MSEIF results ofregularization (eq. (46)) for three systems: the “Blurred Trajectories”(Traj)—graph C1, the “Rim-ring” with quadratic phase coefficient (graphC2), and the “study case” (SC) without additional optical design (graphC3), graph C4 being a reference value As in the previous study case, foreach SNR value (main system SNR), the comparison includes a set of 2400measurements of 8 different objects (see FIG. 13C). The restoration SNRis the “main SNR”, and as in the first study case the auxiliary systemSNR is 16 [db] lower. The “Rim-ring” solution is designed in a similarway as in the first study case, but has 100% transmission for both“lens” and “Rim-ring” and a quadratic phase coefficient of 0.0022 mm.The results show, as expected, a higher MSEIF for a system with a bettercondition. For a condition number of κ=96.8, the quadratic “Rim-ring” isthe best, followed closely by the “blurred Trajectories” where κ=238.7.The last is the study case (κ=6412.5). As explained above, as the SNRdecreases the regularization truncation cuts more of the auxiliary lensinfluence, and so the two “parallel optics” solutions (quadraticRim-ring and blurred Trajectories) asymptotically tend to the mainsystem solution.

FIG. 21 exemplifies typical “object”, “main” system images, “auxiliary”system image, “Blurred Trajectories” restoration, “study case”restoration without additional optical design (as is), and quadratic“Rim-ring” restoration, all in SNR=45 [db]. It is seen that thequadratic “Rim-ring” restoration (RR-Res.) and the “blurredTrajectories” restoration (Traj-Res.) present similar restorationlevels. For objects, such as 3, 4, 6, 7, 8, which seem to contain highspatial frequencies, the latter visually presents a significantly betterrestoration than the study case restoration by regularization alone(SC-Res.).

Turning back to eq. (47), the size of the three pixel blur in theauxiliary system is 33 μm. This could be a diameter of a diffractedlimited, airy disk:

$\begin{matrix}{\frac{33\mspace{11mu}\lbrack{µm}\rbrack}{2 \cdot {0.69\mspace{11mu}\lbrack{mm}\rbrack}} = \left. {0.61 \cdot \frac{0.5875\mspace{14mu}\lbrack{µm}\rbrack}{0.5{D_{app}\mspace{11mu}\lbrack{mm}\rbrack}}}\Rightarrow{D_{app}\bullet\;{0.03\mspace{11mu}\lbrack{mm}\rbrack}} \right.} & (48)\end{matrix}$

The result in (48) shows that the “blurred Trajectories” might beachieved by a micro lenses array (example of FIG. 11B above) whichincludes a field stop, where each “Trajectory” shift is realized by abending prism, and the weight factor (44) by local transmission.

Also, as indicated above, since the “blurred Trajectories” techniqueuses a mixture of two highly blurred images to create a relatively sharpimage, an optical design may be selected which uses the “main” systemimages (generally at least two image) as the main and the auxiliaryimages, i.e. resolves the system by a so-called “double exposure”.

The pixel confined “Trajectories” (or “transformation”) model can beextended to the “blurred Trajectories” model. The simulations have shownthat the “blurred Trajectories” can improve system condition and imagerestoration, by Tikhonov regularization. The invented technique providessignificant improvement in the system matrix condition (from κ=87640down to κ=1211 in the first study case and from κ=6412.5 down to κ=238.7in the second study case). Since the “blurred Trajectories” filtering isdone by software and not by hardware in general, it is much moreflexible than the traditional optical filtering approach. The concept ofcombining two blurred images enables the “Blurred trajectories” methodto produce a system where the “main” and “auxiliary” images are producedby a single aperture system.

Thus, the “Image fusion” technique of the present invention provides forimproving the matrix condition of the imaging system. For example, the“Rim-ring” or the “ping-pong” method can be used to work on a differentauxiliary system (designated as O). All these methods obey the Paralleloptics rule:I ^(image) =H·I ^(object) +O·I ^(object)  (49)which is actually fusion of to systems the goal of which is to improvethe system matrix condition, the main image is:I ^(image) ^(_) ^(main) =H·I ^(object)  (50)and the auxiliary image is:I ^(image) ^(_) ^(Aux) =O·I ^(object)  (51)The fused image is: I ^(image) =I ^(object) ^(_) ^(main) +I ^(image)^(_) ^(Aux)  (52)where (38) is a fused image virtually captured by H1 of a system whichhas improved matrix condition.H1=H+O  (53)H being the main system and O being the auxiliary system.

Turning back to FIG. 1, there was illustrated an example of using theRim-ring design of the auxiliary optical system for improving the imagequality of the imaging system. The following is another example of theRim-ring approach.

In this example, the main lens system is similar to the above-describedone (10 in FIG. 1), namely is an ill conditioned space variant system.The SVD technique is used for defining the system BMSD. Such system BMSDcomposed from the last 50 eigen matrixes is illustrated in FIG. 22.

Each row is converted into 2D image, PSF(x,y,i). The rim-ring filter issingle, assuming a phase effect induced thereby is more dominate thanthat of the main lens aberrations in the rim-ring zone, and it togetherwith the main lens creates a point spread function which is common toall field points and approximately centered around the paraxialcoordinate (“filter's PSF”). The filter's PSF design is preferably suchas to compromise between the different PSF(x,y,i) shapes. For example,such a filter may be constructed by weighted superposition ofPSF(x,y,i), one possible implementation being an average filter.

Then, in order to calculate the average filter, each PSF(x,y,i) isshifted such that its paraxial coordinate (x_(pr),y_(pr)) is transferredto the center of the field (x=0,y=0). This is illustrated in FIG. 23. Anaverage value of the ensemble is applied (54):

$\begin{matrix}{{{PSF}_{target}\left( {x,y} \right)} = {\frac{1}{K}{\sum\limits_{i = 1}^{K}{w_{i\;} \cdot {{psf}\left( {{x - x_{paraxial}},{y - y_{paraxial}},i} \right)}}}}} & (54)\end{matrix}$

Negative values, which are not a physical response for incoherent pointspread function, are truncated, and a target PSF for the auxiliarysystem is define as shown in FIG. 24. The filter producing that targetPSF can be calculated according to Gershberg and Saxton method forRim-ring or for full aperture. This is illustrated in FIG. 25 showingthe phase profile for the rim ring implementation condition numberdropped from nearly 87000 to about 4900, in some cases the conditionnumber drops to about 3977. Then, the new mask can be implemented in the“Rim-ring” or in a separate auxiliary system according to the abovedescribed parallel optics implementation.

This method can serve for calculating a separate auxiliary system O suchthat its implementation will be suitable for use in imaging device whichincludes two separate optical systems such as in the above-describedexamples of the Trajectories method of FIG. 12. In that case the mask ismounted on the auxiliary lens.

Reference is made to FIGS. 26A to 26D exemplifying MSEIF profiles andperformance of the main lens systems equipped and not equipped with anaverage Rim-ring filter.

FIGS. 26A and 26C exemplify two sets of MSEIF equation (45) profiles forthe imaging device where the main lens system is equipped (graphs G1)and is not equipped (graphs G2) with an Average Rim-ring filter. FIG.26A utilizes similar aberration coefficients of the system with orwithout the rim and suffer from different decanter, while FIG. 26Cutilizes a different aberration model including the change in aberrationcoefficients due to the rim. The figures shows 20 db difference in zerocrossing. The restoration was done by simple matrix inversing, ensemblesize N=1000 in each SNR level. FIGS. 26B and 26D show the performance(image restoration) for lenses with and without Average PP Rim-ringfilter restoration example in SNR=60 db and in SNR=65 db for FIGS. 26Band 26D respectively.

Those skilled in the art will readily appreciate that variousmodifications and changes can be applied to the embodiments of theinvention is hereinbefore described without departing from its scopedefined in and by the appended claims.

The invention claimed is:
 1. A system comprising, an imaging system anda processor utility, the imaging system comprising: first and secondlens systems having corresponding first and second numerical aperturesand both directed for imaging of a common object plane, the secondnumerical aperture is smaller than the first numerical aperture; animage detection unit for detecting light collected by the first andsecond lens systems and generating corresponding first and second imagedata pieces indicative of the object plane from a common direction; andthe processor utility is configured to: receive the first image datapiece and the second image data piece that are collected with respect tothe common object plane, and process the first and second image datapieces to provide a reconstructed image of the field of view.
 2. Thesystem of claim 1, wherein the processing of the first and second imagedata pieces includes: generating shifted data indicative of shiftedduplications of the second image data piece with respect to the firstimage data piece, determining combined image data corresponding to thefirst image data piece and the shifted data, and generatingreconstructed image data based on the combined image data.
 3. The systemof claim 1, wherein the first and second lens systems have paralleloptical axes being spaced apart from each other.
 4. The system of claim1, wherein the image detection unit comprises a common light detectorarray configured for collecting image data associated with the first andsecond image data pieces.
 5. The system of claim 1, wherein the imagedetection unit comprises first and second light detectors associatedwith the first and second lens systems.
 6. The system claim 1, whereinthe first and second lens systems are configured to operateconcurrently.
 7. The system of claim 1, wherein the second lens systemcomprises a plurality of separate lenses with shifted fields of viewenabling together light collection from a field of view at leastpartially similar to that of the first lens system.
 8. The system ofclaim 1, configured as a hand held camera unit.
 9. The system of claim1, configured as a smartphone device.